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EPIA
2007
Springer

Generalization and Transfer Learning in Noise-Affected Robot Navigation Tasks

10 years 8 months ago
Generalization and Transfer Learning in Noise-Affected Robot Navigation Tasks
Abstract. When a robot learns to solve a goal-directed navigation task with reinforcement learning, the acquired strategy can usually exclusively be applied to the task that has been learned. Knowledge transfer to other tasks and environments is a great challenge, and the transfer learning ability crucially depends on the chosen state space representation. This work shows how an agent-centered qualitative spatial representation can be used for generalization and knowledge transfer in a simulated robot navigation scenario. Learned strategies using this representation are very robust to environmental noise and imprecise world knowledge and can easily be applied to new scenarios, offering a good foundation for further learning tasks and application of the learned policy in different contexts.
Lutz Frommberger
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where EPIA
Authors Lutz Frommberger
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